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        <title>Context-aware Entity Typing in Knowledge Graphs</title>
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    <name type="personal">
        <namePart type="given">Weiran</namePart>
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    <name type="personal">
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Knowledge graph entity typing aims to infer entities’ missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities’ contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/CCIIPLab/CET.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.193</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Context-aware Entity Typing in Knowledge Graphs
%A Pan, Weiran
%A Wei, Wei
%A Mao, Xian-Ling
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F pan-etal-2021-context-aware
%X Knowledge graph entity typing aims to infer entities’ missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities’ contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/CCIIPLab/CET.
%R 10.18653/v1/2021.findings-emnlp.193
%U https://aclanthology.org/2021.findings-emnlp.193/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.193
%P 2240-2250

Markdown (Informal)

[Context-aware Entity Typing in Knowledge Graphs](https://aclanthology.org/2021.findings-emnlp.193/) (Pan et al., Findings 2021)

• Context-aware Entity Typing in Knowledge Graphs (Pan et al., Findings 2021)

ACL

• Weiran Pan, Wei Wei, and Xian-Ling Mao. 2021. Context-aware Entity Typing in Knowledge Graphs. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2240–2250, Punta Cana, Dominican Republic. Association for Computational Linguistics.